title: Annotation for information extraction from mammography reports.
abstract: Inter and intra-observer variability in mammographic interpretation is a challenging problem, and decision support systems (DSS) may be helpful to reduce variation in practice. Since radiology reports are created as unstructured text reports, Natural language processing (NLP) techniques are needed to extract structured information from reports in order to provide the inputs to DSS. Before creating NLP systems, producing high quality annotated data set is essential. The goal of this project is to develop an annotation schema to guide the information extraction tasks needed from free-text mammography reports.
authors.affiliation: Biostatistics and Medical Informatics, Akdeniz University, Antalya, Turkey.
authors.forename: Selen
authors.initials: S
authors.lastname: Bozkurt
authors.forename: Kemal Hakan
authors.initials: KH
authors.lastname: Gulkesen
authors.forename: Daniel
authors.initials: D
authors.lastname: Rubin
mesh_terms: D000465:Algorithms; D004282:Documentation / Q000379:methods*; D005260:Female; D055991:Health Records, Personal; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008327:Mammography / Q000379:methods*; D011873:Radiology Information Systems